AI-900 Learning Path

Custom Vision

Use this service to train custom image classification and object detection models using your own images.

Translator Text

Use this service to translate text between more than 60 languages.

Anomaly detection

The capability to automatically detect errors or unusual activity in a system. -a machine learning based technique that analyzes data over time and identifies unusual changes.

Natural language processing

The capability for a computer to interpret written or spoken language, and respond in kind.

Mean Absolute Error (MAE)

The average difference between predicted values and true values. This value is based on the same units as the label. The lower this value is, the better the model is predicting.

automated machine learning

capability that leverages the scalability of cloud compute to automatically try multiple pre-processing techniques and model-training algorithms in parallel to find the best performing supervised machine learning model for your data

Key elements of AI

-Machine learning -Anomaly detection -Computer vision -Natural language processing -Conversational AI

Computer vision

The capability of software to interpret the world visually through cameras, video, and images.

Machine learning

-This is often the foundation for an AI system, and is the way we "teach" a computer model to make prediction and draw conclusions from data. -the foundation for most artificial intelligence solutions, and the creation of an intelligent solution often begins with the use of machine learning to train a predictive model using historic data that you have collected.

Regression

-a form of machine learning that is used to predict a numeric label based on an item's features. -a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

Clustering

-is a form of machine learning that is used to group similar items into clusters based on their features -unsupervised machine learning, in which you train a model to separate items into clusters based purely on their characteristics, or features.

Natural language processing (NLP)

-is the area of AI that deals with creating software that understands written and spoken language. -enables you to create software that can: -Analyze and interpret text in documents, email messages, and other sources. -Interpret spoken language, and synthesize speech responses. -Automatically translate spoken or written phrases between languages. -Interpret commands and determine appropriate actions.

A bank wants to use historic loan repayment records to categorize loan applications as low-risk or high-risk based on characteristics like the loan amount, the income of the borrower, and the loan period. What kind of machine learning model should the bank use automated machine learning to create? A. Classification B. Regression C. Time series forecasting

A

You are designing an AI application that uses computer vision to detect cracks in car windshields, and warns drivers when a windshield should be repaired or replaced. When tested in good lighting conditions, the application successfully detects 99% of dangerously damaged glass. Which of the following statements should you include in the application's user interface? A. When used in good lighting conditions, this application can be used to identify potentially dangerous cracks and defects in windshields. If you suspect your windshield is damaged, even if the application does not detect any defects, you should have it inspected by a professional. B. This application detects damage in your windshield. If the application detects a defect, have the windshield replaced or repaired. If no defect is detected, you're good to go! C. This application detects damage in any glass surface, but you must accept responsibility for using it only in appropriate lighting conditions.

A

You are using an Azure Machine Learning designer pipeline to train and test a K-Means clustering model. You want your model to assign items to one of three clusters. Which configuration property of the K-Means Clustering module should you set to accomplish this? A. Set Number of Centroids to 3 B. Set Random number seed to 3 C. Set Iterations to 3

A

You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service? A. Create an inference pipeline from the training pipeline B. Add an Evaluate Model module to the training pipeline C. Clone the training pipeline with a different name

A

You want to create a model to predict sales of ice cream based on historic data that includes daily ice cream sales totals and weather measurements. Which Azure service should you use? A. Azure Machine Learning B. QnA Maker C. Text Analytics

A

You want to use automated machine learning to train a regression model with the best possible R2 score. How should you configure the automated machine learning experiment? A. Set the Primary metric to R2 score B. Block all algorithms other than GradientBoosting C. Enable featurization

A

Azure Machine Learning Feature: Azure Machine Learning designer

A graphical interface enabling no-code development of machine learning solutions.

Relative Absolute Error (RAE)

A relative metric between 0 and 1 based on the absolute differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. This metric can be used to compare models where the labels are in different units.

Relative Squared Error (RSE)

A relative metric between 0 and 1 based on the square of the differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. Because this metric is relative, it can be used to compare models where the labels are in different units.

An automobile dealership wants to use historic car sales data to train a machine learning model. The model should predict the price of a pre-owned car based on its make, model, engine size, and mileage. What kind of machine learning model should the dealership use automated machine learning to create? A. Classification B. Regression C. Time series forecasting

B

Why do we split our data into training and validation sets? A. Data is split into two sets in order to create two models, one model using the training set and a different model using the validation set. B. Splitting data into two sets enables you to compare the labels that the model predicts with the actual known labels in the original dataset. C. We only need to split our data when we use the Azure Machine Learning Designer, not in other machine learning scenarios.

B

You are creating a training pipeline for a regression model, using a dataset that has multiple numeric columns in which the values are on different scales. You want to transform the numeric columns so that the values are all on a similar scale based relative to the minimum and maximum values in each column. Which module should you add to the pipeline? A. Select Columns in a Dataset B. Normalize Data C. Clean Missing Data

B

You are using Azure Machine Learning designer to create a training pipeline for a binary classification model. You have added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that was not used for training. Which additional kind of module should you add? A. Join Data B. Split Data C. Select Columns in Dataset

B

You use Azure Machine Learning designer to create a training pipeline for a clustering model. Now you want to use the model in an inference pipeline. Which module should you use to infer cluster predictions from the model? A. Score Model B. Assign Data to Clusters C. Train Clustering Model

B

You want to train a model that classifies images of dogs and cats based on a collection of your own digital photographs. Which Azure service should you use? A. Azure Bot Service B. Custom Vision C. Computer Vision

B

You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model's performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.3. What can you conclude about the model? A. The model can explain 30% of the variance between true and predicted labels. B. The model predicts accurately for 70% of test cases. C. The model performs worse than random guessing.

C

Azure Machine Learning Feature: Data and compute management

Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale.

Azure Machine Learning Feature: Pipelines

Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks.

Conversational AI

The capability of a software "agent" to participate in a conversation.

Root Mean Squared Error (RMSE)

The square root of the mean squared difference between predicted and true values. The result is a metric based on the same unit as the label. When compared to the MAE, a larger difference indicates greater variance in the individual errors (for example, with some errors being very small, while others are large).

Object detection

These machine learning models are trained to classify individual objects within an image, and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicle.

QnA Maker

This cognitive service enables you to quickly build a knowledge base of questions and answers that can form the basis of a dialog between a human and an AI agent.

Azure Machine Learning Feature: Automated machine learning

This feature enables non-experts to quickly create an effective machine learning model from data.

Face detection, analysis, and recognition

This is a specialized form of object detection that locates human faces in an image. This can be combined with classification and facial geometry analysis techniques to infer details such as age and emotional state; and even recognize individuals based on their facial features.

Optical character recognition (OCR)

This is a technique used to detect and read text in images. You can use it to read text in photographs (for example, road signs or store fronts) or to extract information from scanned documents such as letters, invoices, or forms.

Semantic segmentation

This is an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with "mask" layers to highlight different vehicles using specific colors.

Coefficient of Determination (R2)

This metric is more commonly referred to as R-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.

Face

This service enables you to build face detection and facial recognition solutions.

Azure Bot Service

This service provides a platform for creating, publishing, and managing bots. Developers can use the Bot Framework to create a bot and manage it with this - integrating back-end services like QnA Maker and LUIS, and connecting to channels for web chat, email, Microsoft Teams, and others.

Text Analytics

Use this service to analyze text documents and extract key phrases, detect entities (such as places, dates, and people), and evaluate sentiment (how positive or negative a document is).

Form Recognizer

Use this service to extract information from scanned forms and invoices.

Speech

Use this service to recognize and synthesize speech, and to translate spoken languages.

Language Understanding Intelligent Service (LUIS)

Use this service to train a language model that can understand spoken or text-based commands.

Image analysis

You can create solutions that combine machine learning models with advanced image analysis techniques to extract information from images, including "tags" that could help catalog the image or even descriptive captions that summarize the scene shown in the image.

Computer Vision

You can use this service to analyze images and video, and extract descriptions, tags, objects, and text.

Azure Machine Learning service

a cloud-based platform for creating, managing, and publishing machine learning models.

Image classification

involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.

Azure Machine Learning

is a cloud service that you can use to train and manage machine learning models.

Machine learning

is a technique that uses mathematics and statistics to create a model that can predict unknown values.